Why You Should Use Cubes

SAS cubes are designed to offer efficient data storage, fast data access, easy data maintenance, and flexibility in data management. While cubes are the format of choice to guarantee fast query response times against your data warehouse, SAS OLAP cubes are also often a very space-efficient choice for data storage. In many cases, a basic cube without additional aggregations can be smaller than the input data because the process of creating the cube consolidates records. SAS OLAP cubes use the hierarchy information for efficient aggregations storage. SAS OLAP cubes also deal efficiently with data sparsity by using virtual placeholders for empty cells. This removes the need for any physical representation of empty cells. A good rule of thumb is, the larger your input data, the greater the storage gain by loading data into a cube.

Loading data into cubes and executing queries against the cube take advantage of the multi-threading capabilities of your server machine. Aggregations are created in parallel at cube build time. The creation of individual aggregations takes advantage of the Parallel Group-By capabilities of the SAS data engine. At query execution, the multi-threading capabilities of your server machine are fully used to concurrently serve queries by multiple users. Both query evaluation and data access are executed in parallel.

A cube is the physical representation of your logical dimensional model. The tools that are provided to update and maintain the cube reflect the multidimensional model, which makes both setup and maintenance of your cube as intuitive as possible. SAS Management Console, a Web-based administrator interface, enables you to set up and manage OLAP servers. SAS OLAP Cube Studio provides the workspace and cube designer tools that you need to create and maintain cubes. You can also use the SAS OLAP procedure to create and maintain cubes in a batch environment.

If you create your own aggregations by using data management tools such as SQL, PROC SUMMARY, or the tools of your preferred relational database management system (RDBMS), then you can link those aggregations to your cubes without replicating the data within the cube. Any queries against those aggregations are executed by the appropriate SQL engine, and take advantage of any capabilities that engine might have. This allows you the flexibility to use the data management tools of your choice. It also enables you to distribute your data for your cube aggregations across multiple database systems, servers, and platforms. If you choose to let the cube builder create the aggregations, then you can control where to store the data and index files for each aggregation.